In this script we assess whether perturbation indicators are associated with any of the technical covariates.
First we read the results generated by
perturbation_propensity.R:
results_file <- paste0(
.get_config_path("LOCAL_SCEPTRE2_DATA_DIR"),
"results/perturbation_propensity_analysis/results.rds"
)
results <- readRDS(results_file)
results
## # A tibble: 9,432 × 9
## paper dataset ntc covariate test_type estimate std_error zvalue pvalue
## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 frangieh co_cult… ONE-… n_nonzero joint -1.60e-3 0.00116 -1.38 0.169
## 2 frangieh co_cult… ONE-… n_umis joint 2.48e-5 0.0000950 0.260 0.795
## 3 frangieh co_cult… ONE-… cluster_x joint 2.82e-1 0.239 1.18 0.238
## 4 frangieh co_cult… ONE-… cluster_y joint 3.62e-1 0.205 1.76 0.0777
## 5 frangieh co_cult… ONE-… s_score joint -1.02e-1 1.95 -0.0523 0.958
## 6 frangieh co_cult… ONE-… g2m_score joint -2.56e+0 1.47 -1.74 0.0812
## 7 frangieh co_cult… ONE-… phaseG2M joint 9.58e-1 0.781 1.23 0.220
## 8 frangieh co_cult… ONE-… phaseS joint 7.95e-2 0.685 0.116 0.908
## 9 frangieh co_cult… ONE-… batchB3 joint 6.37e-1 0.631 1.01 0.312
## 10 frangieh co_cult… ONE-… batchC3 joint -3.76e-1 0.768 -0.489 0.625
## # … with 9,422 more rows
Next, for each dataset, we plot the \(p\)-values for each covariate across NTCs: